Betting on next tech wave

THE global technology sector is expected to broaden beyond artificial intelligence (AI) as investors look for opportunities in companies that can translate technological advances into sustainable earnings and cash flow.

Heading into 2027, the focus is likely to shift towards businesses with attractive valuations, strong balance sheets and the ability to generate returns from their investments, rather than simply those spending the most on AI.

Columbia Threadneedle global technology portfolio managers Rahul Narang and Vimal Patel say investors need to look beyond the excitement surrounding AI and pay closer attention to the financial strength of companies supporting its development.

For Patel, interest rates and their underlying drivers are among the biggest considerations, particularly as the massive AI infrastructure expansion increasingly depends on external financing.

“What is driving the rates is the question – it’s tied to oil and then oil is tied to ongoing geopolitical tensions. This AI capital expenditure build-out is dependent on debt capital along with operating cash flow from the big hyperscalers,” he says.

Narang, who has more than 30 years of experience as a technology investor, believes developments in credit markets are becoming just as important as technological breakthroughs.

“I don’t believe there has ever been a more important time to follow what is happening in the credit markets,” he says.

Sustainability factor

Revenue growth, profitability, operating cash flows and the cost and availability of capital will determine how sustainable the current AI investment cycle is.

The distinction between large hyperscalers such as Microsoft, Alphabet and Amazon, which have strong balance sheets and cash flow, and newer AI infrastructure providers that rely more heavily on external financing is becoming increasingly apparent.

Patel says most of the trillion dollars in hyperscaler capital expenditure (capex) comes from four companies with healthy balance sheets and operating cash flow.

Their debt issuances are largely investment grade and supplement existing cash generation, allowing them to continue investing even if interest rates move higher.

However, smaller neocloud operators, many of which are former bitcoin miners, warrant greater caution.

“They don’t have operating cash flow and are predominantly reliant on the debt markets to fund their investments,” he says.

Narang says recent stress among several neocloud providers highlights the need to examine the capital structures supporting AI demand, rather than focusing solely on the growth prospects of the technology.

“Understanding the cost of capital is becoming just as important as understanding the technology itself,” he says.

This is also influencing how investors approach technology stocks heading into 2027.

After an initial period in which semiconductor and AI infrastructure companies outperform while software stocks lag on disruption fears, market leadership is beginning to broaden.

Narang says investors are increasingly distinguishing between software business models, with cybersecurity and infrastructure software emerging as potential beneficiaries of AI adoption.

“Investors will increasingly focus on which companies can convert AI investments into durable revenue growth, earnings and cash flow,” he says.

Patel expects previously overlooked technology stocks to attract greater attention as the market recognises that the investment opportunity extends beyond AI infrastructure spending.

He sees considerable value in companies with strong cash flow, attractive valuations and sound capital allocation programmes.

However, not every technology stock is likely to recover.

Patel says the market has already punished several software categories, initially over concerns about software-as-a-service businesses and subsequently the potential impact of agentic AI on Internet companies.

“Not all software is going to rebound, but we’re focused on infrastructure and vertical software as opposed to application software,” he says.

Narang agrees that the ability to maintain a competitive advantage is becoming more important as AI changes the technology landscape.

Competitive advantage

Companies that rely on a single competitive advantage, particularly some application software providers, face greater disruption risks as large language models and personal AI agents reshape existing business models.

Against this backdrop, the managers stress the importance of active stock selection, particularly in an environment where financing costs could remain elevated.

Patel says investors should avoid buying technology stocks indiscriminately as companies increasingly experience different operating and financial conditions.

“We are seeing a bifurcation in the tech market and that in a higher interest rate environment we need to be more active stock pickers. Rates matter and therefore business models matter too,” he says.

Narang says valuations across the technology sector are not significantly different from their long-term averages, but investors should focus on businesses capable of generating sustainable returns on their AI investments, rather than those announcing the largest spending commitments.

Beyond the United States, both managers are also looking for opportunities in markets with established technology ecosystems.

Patel says the investment approach centres on identifying the strongest technology company in each industry, regardless of its geographical location.

The team is spending more time looking for ideas in Japan, South Korea and Taiwan.

Narang also highlights opportunities in these markets, as well as the Netherlands, where technology companies with sustainable competitive advantages and high barriers to entry and scale are attracting attention.

“We’re looking for sustainable competitive advantages, high barriers to entry, high barriers to scale and strong returns.

“Focusing on the moat of the business has been our north star and has served our clients well,” he says.

Separating genuine innovation from hype is another important part of the investment process.

Narang says the team places considerable emphasis on identifying what could go wrong, drawing on his experience as a short seller to challenge consensus expectations and assess downside risks.

“Great technologies do not always make great investments if the underlying business model or financing structure is not sustainable,” he says.

The managers assess revenue growth, the value proposition of new technologies, feedback from end users and how quickly established companies respond to emerging competitors.

Patel says the research process combines industry and company-level analysis, supported by a venture team that examines private investment opportunities.

The team looks for growth at a reasonable price, with particular attention to free cash flow, earnings before interest, taxes, depreciation and amortisation and earnings per share.

“We want to own real companies with real earnings at reasonable prices.

“In addition, we want to marry our valuation discipline with a strong product cycle that can yield enhanced earnings over time,” he says.

Meanwhile, government regulation, geopolitical tensions, energy prices and AI safety remain among the risks that could influence the sector over the coming year.

Narang says AI demand is likely to experience periods of volatility as companies digest earlier investments and investor sentiment changes.

Hyperscaler capex growth is also expected to moderate at some point, making it important to distinguish temporary market fluctuations from more significant changes in business fundamentals.

For Patel, the central consideration when assessing a technology investment is whether the company can remain relevant as the industry evolves.

“We believe companies with robust multi-year secular growth profiles and strong barriers to entry, specifically those that are among only a handful of companies doing what they do, are likely to benefit over the longer term,” he says.